La financiación autonómica de régimen general: nivelación y corresponsabilidad fiscal desde una perspectiva comparada
Bibliographic record
Abstract
RESUMEN En España, y, en general, en los países federales, ha sido y es común debatir sobre la corresponsabilidad fiscal y sobre los efectos distributivos del sistema de financiación regional. En este trabajo, por un lado, se propone una metodología para estimar la corresponsabilidad fiscal, y por otro, se aplican los instrumentos tradicionales de medida de la desigualdad y la progresividad a los sistemas de financiación de los Estados en varios países federales con el objetivo de realizar una comparación internacional del régimen general de financiación autonómica. Después de un apartado de introducción, en el segundo apartado se describen los instrumentos y metodología usadas para medir la corresponsabilidad y la desigualdad y progresividad. En los apartados tercero y cuarto, se evalúan la corresponsabilidad y desigualdad y progresividad respectivamente en el sistema español y en los sistemas de cuatro países federales: Alemania, Australia, Canadá y Suiza. En el quinto apartado, emprendemos una evaluación simultánea de ambos aspectos analizados. Finalmente, en el apartado sexto resumimos las conclusiones principales del trabajo. ABSTRACT Fiscal accountability and the distributive effects of the regional financing system are common topic of debate in Spain, and, in general, in every federal country. In this paper, on the one hand, we propose a methodology for estimating the fiscal accountability and, on the other, we apply inequality and progressiveness traditional measurement tools to the study of financing systems among States in federal countries for making an international comparison of the Spanish system. After an introduction, in the second part we describe the tools and methodology used for measuring accountability, inequality and progressiveness. In the third and fourth parts we evaluate such concepts in the Spanish system and in four federal countries: Germany, Australia, Canada and Switzerland. In the fifth part, we make a simultaneous evaluation of both aspects analysed. Finally, in the sixth part we summarize the conclusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".